Advising diabetes medications

ABSTRACT

A method includes obtaining prescribing drug information and published guidelines for each of a plurality of ADMs available for managing glucose levels, and receiving patient information associated with a patient. The method also includes ordering total demerit values from lowest to highest, selecting a predetermined number of recommended ADMs associated with the lowest total demerit values, and determining a recommended dosage for each recommended ADM. The method also includes transmitting a therapy regimen to a patient device associated with the patient. The therapy regimen includes the recommended ADMs and the recommended dosage for each recommended ADM.

CROSS REFERENCE TO RELATED APPLICATIONS

This U.S. patent application is a continuation of, and claims priorityunder 35 U.S.C. § 120 from, U.S. patent application Ser. No. 18/089,380,filed on Dec. 27, 2022, which is a continuation of U.S. patentapplication Ser. No. 16/222,415, filed on Dec. 17, 2018, which claimspriority under 35 U.S.C. § 119(e) to U.S. Provisional Application62/609,326, filed on Dec. 21, 2017. The disclosures of these priorapplications are considered part of the disclosure of this applicationand are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

This disclosure relates to managing anti-diabetes medications (ADMs).

BACKGROUND

Diabetes is among the most prevalent and expensive medical conditionsthat requires prescription therapy. Managing diabetes requiresmaintaining glucose levels within a prescribed goal range. For patientswith type 1 diabetes, where the production of insulin is impaired, theaffected individual must regularly inject insulin into the body tomaintain control glucose levels. In contrast to type 1 diabetes,individuals having type 2 diabetes may produce insulin; however, thepancreas may not secrete enough insulin and/or the cells of the body maybe insulin resistant. Accordingly, type 2 diabetes may be treated withone or more of: insulin injections; lifestyle changes, such as exerciseand diet; and anti-diabetes medications (ADMs).

Anti-diabetes medications may include agents configured to increase theamount of insulin secreted by the pancreas, lower resistance of thetarget organs to insulin, and/or lower a rate at which glucose isabsorbed from the gastrointestinal tract. Selection of anti-diabetesmedications generally includes consideration of a variety of factors,including cost, efficacy, effectiveness, complexity of administration,patient lifestyle, interactions of the medication with othermedications, and potential side effects, for example. Accordingly,selection and management of ADMs in combination with other treatmentoptions can be complex.

Hyperglycemia is a condition that exists when blood sugars are too high.While hyperglycemia is typically associated with diabetes, thiscondition can exist in many patients who do not have diabetes, yet haveelevated blood sugar levels caused by trauma or stress from surgery andother complications from hospital procedures. Insulin therapy is used tobring blood sugar levels back into a normal range.

Hypoglycemia may occur at any time when a patient's glucose level isbelow a preferred target. Appropriate management of glucose levels forcritically ill patients reduces co-morbidities and is associated with adecrease in infection rates, length of hospital stay, and death. Thetreatment of hypoglycemia may differ depending on whether or not apatient has been diagnosed with Type 1 diabetes mellitus, Type 2diabetes mellitus, gestational diabetes mellitus, or non-diabetic stresshypoglycemia. The glucose target range BG_(TR) is defined by a lowerlimit, i.e., a low target BG_(TRL) and an upper limit, i.e., a hightarget BG_(TRH).

SUMMARY

One aspect of the disclosure provides a method for determining a therapyregimen. The method includes obtaining, by data processing hardware,prescribing drug information and published guidelines for each of aplurality of Anti-Diabetes Medications (ADMs) available for managingglucose levels and receiving, at the data processing hardware, patientinformation associated with a patient seeking selection and dosing ofone or more of the available ADMs. For each of the available ADMs, themethod further includes: determining, by the data processing hardware,an adverse demerit value, a guideline demerit value, and an instructiondemerit value based on the patient information, the prescribing druginformation, and the published guidelines for the correspondingavailable ADM; and determining, by the data processing hardware, a totaldemerit value by summing the adverse demerit value, the guidelinedemerit value, and the instruction demerit value. The method alsoincludes ordering, by the data processing hardware, the total demeritvalues for the available ADMs from lowest to highest; selecting, by thedata processing hardware, a predetermined number of recommended ADMsassociated with the lowest total demerit values from the plurality ofavailable ADMs; determining, by the data processing hardware, arecommended dosage for each recommended ADM based on the patientinformation, the prescribing drug information, and the publishedguidelines; and transmitting the therapy regimen from the dataprocessing hardware to a patient device associated with the patient. Thetherapy regimen includes the recommended ADMs and the recommended dosagefor each recommended ADM.

Implementations of the disclosure may include one or more of thefollowing optional features. In some implementations, the patientinformation includes at least one of treatment preference information,treatment guideline ratings, a current medications list, current medicalconditions associated with the patient, permanent medical conditionsassociated with the patient, one or more glucose values for the patient,or an A1c value for the patient. The treatment preference informationincludes at least one of a target glucose range for the patient, atarget A1c value for the patient, a preferred minimum monthly treatmentcost, or a preferred maximum monthly treatment cost. The treatmentguideline ratings are each assigned by the patient and measure asubjective level of importance to the patient for a correspondingtreatment guideline. The treatment guideline ratings include at leastone of a cost rating, a body weight rating, a treatment regimencomplexity rating, a treatment efficacy rating, a mealtime coverageneeds rating, or a hypoglycemia rating. The current medications listincludes a list of medications and corresponding dosages the patient iscurrently prescribed. The one or more glucose values for the patient aremeasured by a glucometer or a continuous glucose monitor incommunication with the data processing hardware.

The method may include receiving, at the data processing hardware,exercise data and adjusting, by the data processing hardware, therecommended dosage for at least one of the recommended ADMs based on thereceived exercise data. The exercise data may be received from a fitnesstracker associated with the patient. In some implementations,determining the adverse demerit value includes obtaining one or morecontraindicating conditions associated with the corresponding availableADM based on the prescribing drug information and the publishedguidelines, obtaining a list of medications that interact with thecorresponding available ADM based on the prescribing drug information,determining whether the patient currently has any of thecontraindicating conditions associated with the corresponding availableADM based on the patient information that includes lab resultsassociated with the patient, determining whether the patient iscurrently taking at least one of the medications that interact with thecorresponding available ADM based on the patient information thatinclude a list of medications the patient is currently taking, assigningan adverse demerit increment value when the patient currently has any ofthe contraindicating conditions associated with the correspondingavailable ADM, assigning the adverse demerit increment value when thepatient is currently taking at least one of the medications thatinteract with the corresponding available ADM, and determining theadverse demerit value for the corresponding available ADM based on a sumof each assigned adverse demerit increment value.

In some examples, determining the guideline demerit value includesobtaining treatment guideline ratings each assigned by the patient thatmeasures a subjective level of importance to the patient for acorresponding treatment guideline, obtaining scaled guideline values forthe corresponding available ADM based on the prescribing druginformation and the published guidelines where each scaled guidelinevalue is associated with a corresponding treatment guideline rating,and, for each treatment guideline rating, multiplying the treatmentguideline rating times the corresponding scaled guideline value and aguideline demerit increment value. In these examples, the treatmentguideline ratings include at least one of a cost rating, a body weightrating, a treatment regimen complexity rating, a treatment efficacyrating, a mealtime coverage needs rating, or a hypoglycemia rating.

For each of the available ADMs, the method may also include determining,by the data processing hardware, whether the patient is currently takingthe corresponding available ADM based on the patient information,wherein the patient information includes a list of medications thepatient is currently taking. When the patient is currently taking thecorresponding available ADM, the method may further include assigning,by the data processing hardware, a low modified demerit value to thecorresponding available ADM and adding, by the data processing hardware,the corresponding available ADM having the low modified demerit value tothe predetermined number of recommended ADMs.

In some examples, for each of the available ADMs, the method furtherincludes obtaining, by the data processing hardware, a list of excludedADMs that the patient is either allergic to or is excluded from thetreatment regimen for the patient and determining, by the dataprocessing hardware, whether the corresponding available ADM is on thelist of excluded ADMs. In these examples, when the correspondingavailable ADM is on the list of excluded ADMs, the method includesassigning, by the data processing hardware, a high modified demeritvalue to the corresponding available ADM and replacing, by the dataprocessing hardware, the total demerit value for the correspondingavailable ADM with the assigned high modified demerit value.

In some implementations, the therapy regimen, when received by thepatient device, causes the patient device to display the recommendedADMs and the recommended dosage for each recommended ADM on a patientinterface executing on the patient device.

Additionally or alternatively, the method may also include transmittingthe recommended dosage for at least one of the recommended ADMs to anadministration device associated with the recommended ADM and incommunication with the data processing hardware. Here, theadministration device includes a doser and an administration computingdevice in communication with the doser. The administration computingdevice may be configured to cause the doser to administer therecommended dosage to the patient. In some examples, the administrationdevice includes a smart pill bottle and the doser includes alocking/dispensing mechanism configured dispense one or more ADM pillsbased on the recommended dosage. In other examples, the administrationdevice includes a smart pen that includes a cartridge containing therecommended ADM, and the doser includes a needle for insertion into thepatient for administering the recommended ADM to the patient via thecartridge.

Another aspect of the disclosure provides a system for determining atherapy regimen. The system includes a patient device associated with apatient and a dosing controller in communication with the patientdevice. The dosing controller includes data processing hardware andmemory hardware in communication with the data processing hardware. Thedosing controller is configured to perform operations that includeobtaining prescribing drug information and published guidelines for eachof a plurality of Anti-Diabetes Medications (ADMs) available formanaging glucose levels and receiving patient information from thepatient device. The patient information is associated with the patientseeking selection and dosing of one or more of the available ADMs. Foreach of the available ADMs, the operations further include: determiningan adverse demerit value, a guideline demerit value, and an instructiondemerit value based on the patient information, the prescribing druginformation, and published guidelines for the corresponding availableADM; and determining a total demerit value by summing the adversedemerit value, the guideline demerit value, and the instruction demeritvalue. The operations also include: ordering the total demerit valuesfor the available ADMs from lowest to highest; selecting a predeterminednumber of recommended ADMs associated with the lowest total demeritvalues from the plurality of available ADMs; determining a recommendeddosage for each recommended ADM based on the patient information, theprescribing drug information, and the published guidelines; andtransmitting the therapy regimen from the data processing hardware tothe patient device. The therapy regimen includes the recommended ADMsand the recommended dosage for each recommended ADM.

Implementations of the disclosure may include one or more of thefollowing optional features. In some implementations, the patientinformation includes at least one of treatment preference information,treatment guideline ratings, a current medications list, current medicalconditions associated with the patient, permanent medical conditionsassociated with the patient, one or more glucose values for the patient,or an A1c value for the patient. The treatment preference informationincludes at least one of a target glucose range for the patient, atarget A1c value for the patient, a preferred minimum monthly treatmentcost, or a preferred maximum monthly treatment cost. The treatmentguideline ratings are each assigned by the patient and measure asubjective level of importance to the patient for a correspondingtreatment guideline. The treatment guideline ratings include at leastone of a cost rating, a body weight rating, a treatment regimencomplexity rating, a treatment efficacy rating, a mealtime coverageneeds rating, or a hypoglycemia rating. The current medications listincludes a list of medications and corresponding dosages the patient iscurrently prescribed. The one or more glucose values for the patient aremeasured by a glucometer or a continuous glucose monitor incommunication with the data processing hardware.

In some implementations, the operations further include receivingexercise data from a fitness tracker associated with the patient andadjusting the recommended dosage for at least one of the recommendedADMs based on the received exercise data. In some examples, determiningthe adverse demerit value includes obtaining one or morecontraindicating conditions associated with the corresponding availableADM based on the prescribing drug information and the publishedguidelines, obtaining a list of medications that interact with thecorresponding available ADM based on the prescribing drug information,and determining whether the patient currently has any of thecontraindicating conditions associated with the corresponding availableADM based on the patient information that includes lab resultsassociated with the patient. In these examples, determining the adversedemerit value further includes determining whether the patient iscurrently taking at least one of the medications that interact with thecorresponding available ADM based on the patient information thatincludes a list of medications the patient is currently taking,assigning an adverse demerit increment value when the patient currentlyhas any of the contraindicating conditions associated with thecorresponding available ADM, assigning the adverse demerit incrementvalue when the patient is currently taking at least one of themedications that interact with the corresponding available ADM, anddetermining the adverse demerit value for the corresponding availableADM based on a sum of each assigned adverse demerit increment value.

In some implementations, determining the guideline demerit valueincludes obtaining treatment guideline ratings each assigned by thepatient that measures a subjective level of importance to the patientfor a corresponding treatment guideline, obtaining scaled guidelinevalues for the corresponding available ADM based on the prescribing druginformation and the published guidelines where each scaled guidelinevalue is associated with a corresponding treatment guideline rating,and, for each treatment guideline rating, multiplying the treatmentguideline rating times the corresponding scaled guideline value and aguideline demerit increment value. In these implementations, thetreatment guideline ratings includes at least one of a cost rating, abody weight rating, a treatment regimen complexity rating, a treatmentefficacy rating, a mealtime coverage needs rating, or a hypoglycemiarating.

For each of the available ADMs, the operations may further includedetermining whether the patient is currently taking the correspondingavailable ADM based on the patient information, wherein the patientinformation includes a list of medications the patient is currentlytaking. When the patient is currently taking the corresponding availableADM, the operations may also include assigning a low modified demeritvalue to the corresponding available ADM and adding the correspondingavailable ADM having the low modified demerit value to the predeterminednumber of recommended ADMs.

In some implementations, for each of the available ADMs, the operationsalso include obtaining a list of excluded ADMs that the patient iseither allergic to or is excluded from the treatment regimen for thepatient and determining whether the corresponding available ADM is onthe list of excluded ADMs. In these implementations, when thecorresponding available ADM is on the list of excluded ADMs, theoperations also include assigning a high modified demerit value to thecorresponding available ADM and replacing the total demerit value forthe corresponding available ADM with the assigned high modified demeritvalue.

In some examples, the therapy regimen when received by the patientdevice causes the patient device to display the recommended ADMs and therecommended dosage for each recommended ADM on a patient interfaceexecuting on the patient device. In some implementations, the operationsalso include transmitting the recommended dosage for at least one of therecommended ADMs to an administration device associated with therecommended ADM and in communication with the data processing hardware.Here, the administration device includes a doser and an administrationcomputing device in communication with the doser. The administrationcomputing device is configured to cause the doser to administer therecommended dosage to the patient. In some examples, the administrationdevice includes a smart pill bottle and the doser includes alocking/dispensing mechanism configured dispense one or more ADM pillsbased on the recommended dosage. In other examples, the administrationdevice includes a smart pen that includes a cartridge containing therecommended ADM and the doser includes a needle for insertion into thepatient for administering the recommended ADM to the patient via thecartridge.

DESCRIPTION OF DRAWINGS

FIG. 1A is a schematic view of an example system for managing glucoselevels of a patient.

FIG. 1B is a schematic view of an example system for managing glucoselevels of a patient.

FIG. 1C is a schematic view of an example administration device incommunication with a dosing controller.

FIG. 1D is a schematic view of example components of the system of FIGS.1A-1C.

FIG. 2 is a schematic view of an example dosing controller configured toexecute instructions to evaluate and select Anti-Diabetes Medications(ADMs) to be included in a treatment regimen for a patient.

FIG. 3A is a schematic view of an example patient data table including aschedule of all patients treated by a respective Health Care Provider(HCP).

FIG. 3B is a schematic view of a permanent condition table for arespective patient including a list of permanent medical conditionsassociated with the patient.

FIG. 4A is a schematic view of a patient preferences table listingtreatment preferences associated with a patient.

FIG. 4B is a schematic view of an allergies and exclusions tableincluding a list of one or more ADMs that a patient is allergic to orthat have been excluded from a treatment regimen for the patient.

FIG. 4C is a schematic view of a current medications table including alist of medications a patient is currently taking.

FIG. 4D is a schematic view of a patient device calibration tablelisting patient devices associated with a patient and calibrationparameters associated with each patient device.

FIG. 4E is a schematic view of a patient device table including healthdata and exercise data obtained from one or more patient devicesassociated with the data.

FIG. 4F is a schematic view of a current conditions table including alist of conditions associated with lab test results for a patient.

FIG. 4G is a schematic view of a current labs table including a recordof lab results for a patient.

FIG. 5A is a schematic view of an ADM table including a list of ADMs andpertinent information for each ADM.

FIG. 5B is a schematic view of a drug interactions table including alist of drugs/medications that interact with one of the ADMs from theADM table of FIG. 5A.

FIG. 5C is a schematic view of an available dosages table for one of theADMs from the ADM table of FIG. 5A.

FIG. 5E is a schematic view of a contraindications table including alist of contraindications associated with ADMs.

FIG. 5F is a schematic view of a guideline refreshment conversionprocess table including a list of guideline values assigned by apatient.

FIG. 5G is a schematic view of a configurable constants table.

FIG. 6A is a schematic view of a patient preferences screen.

FIG. 6B is a schematic view of an allergies and conditions screenindicating ADMs a patient is allergic to.

FIG. 6C is a schematic view of an energy-based dose adjustment screenfor adjusting ADM dosages based on exercise.

FIG. 6D is a schematic view of an ADM selection screen displaying atreatment regimen for a patient that includes a list of recommended ADMsand recommended dosages for each recommended ADM.

FIG. 7 is a schematic view of an ADM selection process for selectingrecommended ADMs for inclusion in a treatment regimen for a patient.

FIG. 8 is a schematic view of an ADM selection table including a list ofavailable ADMs.

FIG. 9 is an exemplary arrangement of operations for selectingrecommended ADMs and dosing for administration to a patient.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Diabetic outpatients affected by type 2 diabetes may maintain theirglucose levels within desired ranges by using various combinations oftherapies that includes injection dosages of insulin, dietary andexercise management, and anti-diabetes medications (ADMs). However, awide variety of ADMs are available for treating type 2 diabetes, each ofwhich may be associated with various characteristics. Therefore, it isdesirable to have a clinical support system 100 (FIGS. 1A and 1B) thatadvises and manages selection and administration of ADMs.

Referring to FIGS. 1A and 1B, in some implementations, a clinicalsupport system 100 analyzes inputted patient condition parameters for anoutpatient 10 and selects and manages a personalized treatment regimento adjust and maintain a glucose level or target A1C of the outpatient10 within a target range. As used herein, the patient refers to anoutpatient that may be located at some remote location, such as thepatient's 10 residence or place of employment. As used herein, the term“clinic” or “clinical” may refer to a location in which care managersprovide healthcare services to patients. The system 100 includes a firstprogram implemented in connection with one or more of: a personalcomputer 110, 110 a of a patient 10; a patient device 110, 110 b (e.g.,mobile phone, tablet); a smart wearable 110, 110 c (e.g., smart watch,fitness tracker); an insulin pump 123, 123 a; a smart pen 123, 123 b;smart pill bottle 123 c; a smart pill 123 d configured to detect andcommunicate ingestion; glucose meter (commonly referred to as“glucometer”) 124; continuous glucose monitor (CGM) 127; a body weightscale 125, a service provider or health care professional (HCP) device140; and/or a service provider 130. The glucose meter 124 and CGM 127may be collectively referred to as a glucose measurement device 124,127.

The system 100 further includes a second program, or dosing controller160, that may reside in one or more of the patient device 110, theservice provider device 140, and or the service provider 130. The dosingcontroller 160 provides advice on the selection and dosing ofAnti-Diabetes Medications (ADMs). The dosing controller 160 may alsoadvise and/or select dosing for insulin injections to manage thepatient's 10 glucose values. Selection and dosing advice is determinedby comparing a health status of the patient 10 to prescribing druginformation 196 and published guidelines 198. The health status incudes:real-time data transmitted by the patient device(s) 110, 123, 124, 125,127; digital downloads from the patient device(s) 110, 123, 124, 125,127; laboratory tests; and judgement-based assessments by the HCP 40 andthe patient 10. The prescribing drug information 196 and publishedguidelines 198 may be from published advisory literature including, butnot be limited to, two types: 1) the Food and Drug Administration (FDA)approved labeling provided by the manufacturer of the ADM as a packageinsert, and 2) guidelines published by advisory institutions such as theAmerican Diabetes Association (ADA) and the American Association ofClinical Endocrinologists (AACE).

The comparison of the health status to the aforementioned references196, 198 is accomplished by the dosing controller 160, which thenprovides an output corresponding to selection and dosing of a treatmentregimen. The results are used to improve glycemic control of the patient10 by adjusting the selection and dosing of the ADMs. Selection anddosing may be controlled automatically by the dosing controller 160, ormay include communicating information to the patient 10 in real-time sothat he/she can manually change his/her ADM regimen.

In addition to selecting and managing ADMs, the dosing controller 160may advise or prescribe changes in a dietary and exercise regimen of thepatient 10. This is accomplished by calculating a net-energy budget thatcompares grams of carbohydrate consumed and calories of energy burned byregimented exercise and in the process of normal living. An excess ordeficit of caloric energy would cause an increase or decrease in theHemoglobin A1c of the patient 10, which is monitored as an indicator.The HCP can prescribe changes in diet and exercise that will adjust theA1c of the patient 10 toward a target range.

Referring to FIGS. 1A and 1B, the clinical support system 100 includes aglycemic management module 50, an integration module 60, a surveillancemodule 70, and a reporting module 80. Each module 50, 60, 70, 80 is incommunication with the other modules 50, 60, 70, 80 via a network 20. Insome examples, the network 20 (discussed below) provides access to cloudcomputing resources that allows for the performance of services onremote devices instead of the specific modules 50, 60, 70, 80. Theglycemic management module 50 executes the program 160 (e.g., anexecutable instruction set) on a computing device 112, 132, 142 or onthe cloud computing resources. The integration module 60 allows for theinteraction of users 40 and patients with the system 100. Theintegration module 60 receives information inputted by a user 40 andallows the user 40 to retrieve previously inputted information stored ona storage system (e.g., one or more of cloud storage resources 24, anon-transitory memory 144 of an electronic medical system 140 of aclinic 42 or telemedicine facility, a non-transitory memory 114 of thepatient device 110, a non-transitory memory 134 of the serviceprovider's system 130, or other non-transitory storage media incommunication with the integration module 60). The storage resources 24and non-transitory memory 114, 134, 144 may individually or collectivelybe referred to as memory hardware. Therefore, the integration module 60allows for the interaction between the HCPs 40, patients 10, and thesystem 100 via a display 116, 146. The surveillance module 70 considerspatient information received from a HCP 40 via the integration module 60and information received from a glucometer 124 or CGM 127 that measuresa patient's glucose value and determines if the patient 10 is within athreshold glucose value. Generally, the glucometer 124 measurescapillary “blood glucose” values and the CGM 127 measures “interstitialglucose” values that can be correlated to blood glucose values. As usedherein, the term “glucose value” refers to either one of blood glucoseor interstitial glucose. Moreover, use of the term “blood glucose” isnot meant to imply that the CGM 127 was not used due to the correlationbetween interstitial glucose and blood glucose. In some examples, thesurveillance module 70 alerts the user 40 if a patient's glucose valuesare not within a threshold glucose value. The surveillance module 70 maybe preconfigured to alert the user 40 of other discrepancies betweenexpected values and actual values based on pre-configured parameters.For example, when a patient's glucose value drops below a lower limit ofthe threshold glucose value. The reporting module 80 may be incommunication with at least one display 116, 146 and providesinformation to the user 40 determined using the glycemic managementmodule 50, the integration module 60, and/or the surveillance module 70.In some examples, the reporting module 80 provides a report that may bedisplayed on a display 116, 146 and/or is capable of being printed.

The system 100 is configured to evaluate a glucose level, a nutritionalintake, and lifestyle of a patient 10. Based on the evaluation andanalysis of the data, the system 100 selects and executes a treatmentregimen, which is administered to the patient 10 to adjust and maintainthe glucose value of the patient 10 into a glucose target range. Thesystem 100 may be applied to various devices, including, but not limitedto, patient devices 110, subcutaneous insulin infusion pumps 123 a,smart pens 123 b, smart pill bottles 123 c, smart pills 123 d,glucometers 124, CGM 127, and smart scales 125. Smart pens 123 b mayinclude ADM pens for injecting ADMs to the patient subcutaneously or mayinclude insulin pens for injecting insulin to the patient 10subcutaneously.

In some examples, the clinical support system 100 includes the network20, the patient device 110, the dosing controller 160, a serviceprovider 130, and a glucose device manufacturer provider 180. Thepatient device 110 may include, but is not limited to, desktop computers110 a or portable electronic device 110 b (e.g., cellular phone,smartphone, personal digital assistant, barcode reader, personalcomputer, or a wireless pad), activity trackers 110 c (e.g., smartwatch, fitness band) or any other electronic device capable of sendingand receiving information via the network 20. In some implementations,one or more of the patient's glucometer 124, CGM 127, insulin pump 123a, pen 123 b, or bottle/cap 123 c are capable of sending and receivinginformation via the network 20.

The patient device 110 a, 110 b, 110 c includes a data processor 112 a,112 b, 112 h (e.g., a computing device that executes instructions),non-transitory memory 114 a, 114 b, 114 h and a display 116 a, 116 b,116 h (e.g., touch display or non-touch display) in communication withthe data processor 112 a, 112 b, 112 h. In some examples, the patientdevice 110 includes a keyboard 118, speakers 122, microphones, mouse,and a camera.

The insulin pump 123 a, pen 123 b, glucometer 124, and CGM 127associated with the patient 10 may include a data processor 112 c, 112d, 112 e, 112 i (e.g., a computing device that executes instructions),and non-transitory memory 114 c, 114 d, 114 e, 114 i, and/or a display116 c, 116 d, 116 e (e.g., touch display or non-touch display) incommunication with the data processor 112 c, 112 d, 112 e, 112 i. Thedevices 123 a, 123 b, 124, 127 may also communicate wirelessly throughthe network 20 and/or with any other patient device 110, 123 a, 123 b,123 c, 124, 125, 127 through the same or different network 20.

The smart scale 125 and the smart bottle 123 c each include a dataprocessor 112 f, 112 g, (e.g., a computing device that executesinstructions). The smart scale 125 and the smart bottle 123 c furtherinclude non-transitory memory 114 f, 114 g and a display 116 f, 116 g(e.g., touch display or non-touch display) in communication with thedata processor 112 f, 112 g.

The clinical support system 100 may also include a glucose devicemanufacturer provider 180 including a data processor 182 incommunication with non-transitory memory 194. The data processor 192 mayexecute a proprietary download program for downloading glucose data fromthe memory 114 c of the patient's glucometer 124 and/or from the memory114 i of the patient's CGM 127. In some implementations, the heal careprovider 140 implements the proprietary download program on a computingdevice 142 or the proprietary download program is implemented on thepatient device 110 for downloading the glucose data from the memory 114c. In some examples, the download program exports a glucose data filefor storage in the non-transitory memory 24, 114, 144. The dataprocessor 182 may execute a web-based application for receiving andformatting glucose data transmitted from one or more patient devices 110a, 110 b, 124, 123 a, 123 b, 123 c, 127 and storing the glucose data innon-transitory memory 24, 114, 144.

The drug manufacturer provider 190 may include a data processor 192 incommunication with non-transitory memory 194. The memory 194 may storethe prescribing drug information 196 and the published guidelines 198,and the data processor 192 may provide the prescribing drug information196 and the published guidelines 198 to the dosing controller 160 foroutputting a corresponding selection and dosing of a treatment regimenfor the patient 10 based on the health status of the patient 10.

The services provider 130 may include a data processor 132 incommunication with non-transitory memory 134. The service provider 130provides the patient 10 with a program 162 (see FIG. 1D) (e.g., a mobileapplication, a web-site application, or a downloadable program thatincludes a set of instructions) executable on a computing device 112,132, 142 of the dosing controller 160 and accessible through the network20 via the patient device 110, health care provider electronic medicalrecord systems 140, portable glucose measurement devices 124, 127 (e.g.,glucose meter, glucometer, or CGM), or portable administration devices123 a, 123 b, 123 c.

In some implementations, the HCP medical record system 140 is located ata doctor's office, clinic 42, or a facility administered by a hospital(such as a hospital call center) and includes a data processor 142, anon-transitory memory 144, and a display 146 (e.g., touch display ornon-touch display). The non-transitory memory 144 and the display 146are in communication with the data processor 142. In some examples, theHCP electronic medical system 140 includes a keyboard 148 incommunication with the data processor 142 to allow a user 40 to inputdata, such as fixed patient data 300 (FIG. 2 ). The non-transitorymemory 144 maintains patient records capable of being retrieved, viewed,and, in some examples, modified and updated by authorized hospitalpersonal on the display 146.

The dosing controller 160 is in communication with the glucosemeasurement devices 124, 127 and the administration devices 123, andincludes a computing device 112, 132, 142 and non-transitory memory 114,134, 144 in communication with the computing device 112, 132, 142. Thedosing controller 160 executes the program 162. The dosing controller160 stores patient related information retrieved from the glucosemeasurement devices 124, 127, patient devices 110, and/or smart scale125 to determine ADM selections and dosing parameters (and insulindosing parameters in some scenarios) based on the received glucosemeasurement and other factors associated with the patient 10, such asactivity level, weight, and/or meal consumption.

Referring to FIG. 1C, in some implementations, the administration device123 (e.g., insulin pen, smart pill bottle/cap, smart pill), incommunication with the dosing controller 160, is capable of executinginstructions for administering insulin and/or ADM(s) according to ananti-diabetes treatment regimen selected by the dosing controller 160.The administration device 123 may include the insulin pump 123 a, thepen 123 b, or the smart pill bottle/cap 123 c. The administration device123 is in communication with the patient devices 110, the glucometer124, the CGM 127, and the smart scale 125 and includes a computingdevice 112 d, 112 e, 112 g and non-transitory memory 114 d, 114 e, 114 gin communication with the computing device 112 d, 112 e, 112 g. Theadministration device 123 includes a doser 223 a, 223 b, 223 g incommunication with the administration computing device 112 d, 112 e, 112g for administering an ADM or insulin to the patient 10. For instance,the doser 223 a of the insulin pump 123 a includes an infusion setincluding a tube in fluid communication with an insulin reservoir and acannula inserted into the patient's 10 body and secured via an adhesivepatch. The doser 223 b of the pen 123 b of the pen 123 b includes aneedle for insertion into the patient 10 for administering an ADM orinsulin to the patient via a cartridge. The doser 223 g of the smartpill bottle/cap 123 c may include a locking mechanism that unlocks thebottle 123 c for administering an ADM pill by the patient 10.Additionally or alternatively, the doser 223 g may include a dispensingmechanism that dispenses one or more ADM pills for administering to thepatient 10. In some examples, the doser 223 g communicates with thedisplay 116 g and/or speaker for presenting a visual and/or audio alertto notify the patient 10 it is time to administer a specified dosage ofone or more ADM pills. The administration device 123 is in communicationwith the dosing controller 160, and receives instructions from thedosing controller relating to administration of recommended dosages ofinsulin or ADMs. Here, the administration computing device 112 d, 112 e,112 g may execute the anti-diabetes treatment regimen selected by thedosing controller 160 and need not be pre-programmed to execute variousanti-diabetes treatment regimens/programs stored within memory 114 d,114 e, 114 g, thereby reducing memory usage while increasing processingspeeds thereof. Thus, executing the anti-diabetes treatment regimen byadministration computing device 112 d, 112 e, 112 g causes the doser 223a, 223 b, 223 b to administer doses of ADMs or insulin specificallytailored for the patient 10 as specified by the anti-diabetes treatmentregimen. Accordingly, the administration devices 123 a, 123 b, 123 c maybe “smart” administration devices capable of communicating with thedosing controller 160 to populate recommended doses of ADMs or insulinfor administering to the patient 10. In some examples, theadministration devices 123 a, 123 b, 123 c execute the dosing controller160 on the administration computing devices 112 d, 112 e, 112 g tocalculate the recommended doses of ADMs or insulin for administering tothe patient 10.

The network 20 may include any type of network that allows sending andreceiving communication signals, such as a wireless telecommunicationnetwork, a cellular telephone network, a time division multiple access(TDMA) network, a code division multiple access (CDMA) network, Globalsystem for mobile communications (GSM), a third generation (3G) network,fourth generation (4G) network, Long-Term Evolution (LTE) network, fifthgeneration (5G) network, a satellite communications network, and othercommunication networks. The network 20 may include one or more of a WideArea Network (WAN), a Local Area Network (LAN), and a Personal AreaNetwork (PAN). In some examples, the network 20 includes a combinationof data networks, telecommunication networks, and a combination of dataand telecommunication networks. The patient device 110, the serviceprovider 130, and the hospital electronic medical record system 140communicate with each other by sending and receiving signals (wired orwireless) via the network 20. In some examples, the network 20 providesaccess to cloud computing resources, which may be elastic/on-demandcomputing and/or storage resources 24 available over the network 20. Theterm ‘cloud’ services generally refers to a service performed notlocally on a user's device, but rather delivered from one or more remotedevices accessible via one or more networks 20.

FIG. 1D is a schematic view of exemplary components of the system 100.In some implementations, the administration device 123 associated withthe patient 10 includes a smart pen 123 b or smart pill bottle 123 cthat is capable of communicating (e.g., syncing) with a patient device110 such as a smart phone 110 b. In the example shown, the smart pen 123b and smart pill bottle 123 c communicate with the smart phone 110 b viaBluetooth, however, other wireless or wired communications are possible.The smart pen 123 b and/or smart pill bottle 123 c may include anassociated smart cap 23 that removably attaches to the respective smartpen 123 b or smart pill bottle 123 c. For instance, the smart cap 23 mayattach to the smart pen 123 b to enclose and protect the doser 223 bwhen not being used to administer the ADM or insulin, and then removedfrom the pen 123 b to expose the doser 223 b when the patient 10 isadministering and ADM or insulin. Similarly, the smart cap 23 may attachto the smart pill bottle 123 c to enclose/seal the ADM pills within thesmart pill bottle 123 c and be removed to provide access to the bottlewhen the patient 10 is administering one or more ADM pills. In someimplementations, the smart cap 23 implements some or all of thefunctionality of the respective smart pen 123 b or smart pill bottle 123c. For instance, the smart cap 23 may include the processor 112 e, 112g, the non-transitory memory 114 e, 114 g and/or the display 116 e, 116g instead of the smart pen and smart pill bottle 123 b, 123 c, or thepen 123 b and/or bottle 123 c may each implement at least one of theprocessor 112 e, 112 g the non-transitory memory 114 e, 114 g and/or thedisplay 116 e, 116 g. Accordingly, the smart cap 23 may communicate withthe patient device 110 (e.g., smart phone 110 b) via Bluetooth orthrough other wireless or wired communications.

In some configurations, the fitness tracker 110 c communicates exercisedata to the smart phone 110 b via Bluetooth, infrared, cable, or othercommunications. The mobile application (e.g., program) 162 may executeon the computing device 112 b of the smart phone 110 b to provide theexercise data to the dosing controller 160. The exercise data mayinclude, without limitation, calories burned, walking steps, runningsteps, miles run, miles walked, and resistance repetitions. The dosingcontroller 160 may use exercise data when determining a recommended doseof an ADM or insulin for the patient to administer. The patient 10 mayadditionally or alternatively input the exercise data into the smartphone 110 b or other device in communication with the smart phone 110 b.

The glucometer 124 and CGM 127 may also communicate glucose measurementsto the smart phone 110 b via Bluetooth, infrared, cable, or othercommunications. The mobile application 1198 executing on the computingdevice 112 b of the smart phone for communicating with the dosingcontroller 160 such that information can be communicated over thenetwork 20 between the dosing controller 160 and each of the smart pillbottle 123 c (and/or cap 23), smart pen 123 b (and/or cap 23), theglucometer 124, the CGM 127, and the fitness tracker 110 c. For example,dosing parameters (dosing information) adjusted by the dosing controller160 may be transmitted to the smart phone 110 b and stored within memory114 b (FIG. 1B). The dosing parameters may include, but are not limitedto: TargetBG; target A1c, recommended basal/bolus doses of insulin;recommended ADM doses and types; and scheduled administration times foradministering doses of ADMs or insulin. The dosing parameters may beadjusted automatically or manually initiated by the user/HCP 40 orpatient 10.

In some implementations, upon the glucometer 124 or CGM 127 determininga glucose measurement, the glucometer 124 or CGM 127 transmits theglucose measurement to the smart phone 110 b. The smart phone 110 b mayrender the glucose measurement upon the display 116 b and permit thepatient 10 to select the BGtype associated with the glucose measurement.The BGtype or BG Interval corresponds to a label or tag chosen by thepatient 10 from a dropdown list upon the display 116 b of the smartphone 110 b. Alternatively, the patient 10 may select the BG Intervalfrom a dropdown list displayed on the display 116 c of the glucometer.The smart phone 110 b may transmit the glucose measurement and the BGtype to the dosing controller 160 via the network 20. In some examples,the glucometer 124 or CGM 127 is configured to transmit the glucosemeasurement and/or BG type directly to the dosing controller 160 via thenetwork 20. The patient 10 may also input meal information, such ascarbohydrates consumed for breakfast, lunch, or dinner, to the smartphone 110 b.

In some examples, the patient 10 may enter a number of carbohydrates fora current meal into the glucometer 124, the CGM 127, or fitness tracker110 c for transmission to the smart phone 110 b or directly into thesmart phone 110 b when a glucose measurement is received. For instance,upon receiving the glucose measurement from the glucometer 124 or theCGM 127, the smart phone 110 b may render an interactive graphic uponthe display 116 b that enables the patient to enter the number ofcarbohydrate grams the patient 10 plans to ingest. The mobileapplication 1198 executing on the smart phone 110 b may provide theglucose measurement and the number of carbohydrate grams to the dosingcontroller 160 for calculating the recommended dose for display on thedisplay 116 b.

In some implementations, a recommended dose is determined by the dosingcontroller 160 and sent to the smart phone 110 b during each adjustmenttransmission and stored within the memory 114 b. The recommended dosemay include one or more ADM pills or a dosage of insulin for the patient10 to administer. Accordingly, upon receiving the recommended dose, themobile application 1198 sends the appropriate number of ADM pills, dosesof ADM, or doses of insulin to the smart pill bottle 123 c or the smartpen 123 b. In some examples, the smart pen 123 b (using theadministration computing device 112 e) automatically dials in the totalnumber of units for the recommended dose of ADM or insulin for the doser223 b to administer. The patient 10 may interact with the smart pen 123b (or cap 23) or smart pill bottle 123 c (or cap 23) to accept therecommended dose displayed upon the display 116 e or manually change therecommended dose. The doser 223 b of the smart pen 123 b may include anelectro-mechanical stop that actuates a plunger to only administer therecommended dosage of ADM or insulin accepted by the patient 10 ordosage of ADM or insulin manually entered by the patient 10. Likewise,the doser 223 g of the smart pill bottle 123 c may include a lockingmechanism that unlocks to dispense a number of ADM pills correspondingto the recommended dosage of ADM. In some examples, upon administrationof an ADM or insulin dose by the administration device 123 (e.g., smartpen 123 b or smart pill bottle 123 c), the administration device 123transmits the value of the administered dose (or bottle access data) andthe time of the administered dose (or bottle access data) to the smartphone 110 b for storage within memory 114 b along with the associated BGmeasurement. Additionally, the smart phone 110 b may transmit theadministered dose (or bottle access data) and the time of theadministered dose (or bottle access data) to the dosing controller 160via the network 20. In some configurations, the smart pen 123 b (or cap23) and/or smart pill bottle 123 c (or cap 23) forms a directcommunication link with the dosing controller 160 via the network 20 forreceiving the recommended dosing information and/or transmitting theadministered dose and the time of the administered dose to the dosingcontroller 160.

In some implementations, an ADM pill includes the ADM smart pill 123 dthat includes the ADM as well as an ingestible sensor 113 that activateswhen in contact with stomach fluid to detect when the patient 10administers the pill. Subsequently, the pill is configured to transmitactivation by the sensor 113 to a wearable patch 115 (or othertransceiver) that transmits the ingestion data to the smart phone 110 b.The application 162 executing on the smart phone 110 c may log thereceived ingestion data along with a corresponding time stamp to allowthe HCP 40 to access the ingestion data to determine if the patient 10is being compliant. The patch 115 may include an adhesive for attachingto the patient skin near the stomach, and a transceiver for receiving anindication that the ingestible sensor 113 has been activated uponingestion and transmitting the ingestion data to the smart phone 110 bor other patient device 110. In some examples, if ingestion data is notreceived by a time threshold for administering the ADM smart pill 123 d,the dosing controller 160 may send an alert to the administration device123 to remind the patient 10 to administer a recommended dosage of theADM pill 123 d in case the patient forgot to administer the pill.

With reference to FIG. 2 , the dosing controller 132, 160 is configuredto execute instructions to evaluate and select ADMs to be included in atreatment regimen based on a plurality of linked tables maintained indata storage 200 of the memory 24, 114, 134, 144. Each of the tables canbe classified into one of three categories: (i) fixed patient data 300;(ii) dynamic patient data 400; and (iii) reference data 500. Tablesincluding fixed patient data 300 are shown in FIGS. 3A and 3B andcontain data permanently associated with each individual patient 10,such as identification, demographics, and permanent medical information,for example. Tables including dynamic patient data 400 containdate-stamped data associated with dates-of-service and changes in thehealth status and therapy of the patient 10. Examples of tablesincluding dynamic patient data 400 are shown in FIGS. 4A-4G. Tablesincluding reference data 500 are applied universally throughout thesystem 100 for all patients 10. The tables including reference data 500contain published information from third-party resources, and areperiodically updated based on revisions by the third-party resource.Examples of tables including reference data 500 are shown in FIGS.5A-5G.

Referring to FIG. 3A, a patient data table 310 of the fixed patient data300 is provided and includes a schedule of all patients 10 treated by arespective HCP 40. The patient data table 310 is linked to a pluralityof sub-tables 320, 410, 420, 430, 440, 450, 450, 470 in a few-to-manyrelationship, whereby data related to each record 312, 312 a-c (i.e.,patient) in the patient data table 310 is stored in each of the varioussub-tables corresponding to the record. For example, the second record312, 312 b associated with Tilly Typical in the patient data table 310of FIG. 3A may be linked to the permanent conditions table 320 shown inFIG. 3B. The permanent conditions table 320 includes a schedule ofpermanent conditions associated with patient Tilly Typical.

Referring back to FIG. 2 , the patient data table 310 is further linkedto a plurality of sub-tables including dynamic patient data 400. Asshown in FIG. 4A, a patient preferences table 410 includes treatmentpreference information 411, 411 a-d and treatment guideline ratings 412,412 a-f for a single one of the patients 10 in the patient data table310. For example, the treatment preference information 411, 411 a-d mayinclude a target glucose (BG Target) 411 a, a target A1c (A1c Target)411 b, a preferred minimum monthly treatment cost (ADM_$_perMo_Low) 411c, and a preferred maximum monthly treatment cost (ADM_$_perMo_Hi) 411d.

The treatment guideline ratings 412 of the patient preferences table 410are associated with an importance of corresponding treatment guidelines.In the illustrated example, the treatment guideline ratings 412 includecost (Cost Importance) 412 a, effect on body weight (Weight Importance)412 b, treatment regimen complexity (Complexity Importance) 412 c,treatment efficacy (Efficacy Importance) 412 d, mealtime coverage needs(Mealtime_Coverage_Importance) 412 e, and risk of hypoglycemia(Hyopglycemia_Importance) 412 f. Each treatment guideline rating (412)is assigned a numeric rating based on the patient's 10 subjective levelof importance for the treatment guideline. In the illustrated example,the importance of the treatment guidelines are rated using a binaryscale, whereby a rating of “0” corresponds to a treatment guidelinehaving little or no importance to the patient, and a rating of “1”corresponds to a treatment guideline having high importance. In someimplementations, importance of each treatment guideline is indicatedbased on a scaled rating. For example, importance may be indicated basedon a scale from 1 to 10, with a value of “1” being associated with alowest level of importance to the patient 10 and a value of “10” beingassociated with a highest level of importance to the patient 10.

The dosing controller 160 may periodically update the patientpreferences table 410 based on feedback received from the patient 10.Here, the patient 10 may provide patient preference feedback to his/herhealthcare provider(s) during office visits, phone consultations, orelectronic communications, and the HCP 40 may provide the patientpreference feedback to the dosing controller 160 to update the patientpreferences table 410. For example, as shown in FIG. 4A, on Jun. 17,2016 the HCP 40 with the surname Pepper updated the patient preferencestable 410 to indicate that treatment cost 412 a was now of highimportance to the patient, and on Jun. 28, 2016 another HCP 40 withsurname Livingston updated the patient preferences table 410 to indicatethat effect on body weight 412 b was of high importance to the patient10.

The patient preferences table 410 may be updated via an interactivepatient preferences screen 610, as shown in FIG. 6A. The patientpreferences screen 610 presents the HCP 40 or a patient 10 with a seriesof questions corresponding to the treatment guideline ratings 412. Forexample, the patient preferences screen 610 may present a first seriesof questions to be answered by the patient 10, including questionsrelated to the importance of an effect on body weight guideline, thetreatment cost guideline, and, if necessary, the minimum and maximummonthly treatment costs. The patient preferences screen 610 may alsoinclude questions to be answered by the HCP 40. For example, theinteractive input may include questions relating to the HCP's judgmentwith respect to the requirement for additional mealtime coverage and theability of the patient to handle a complex treatment regimen. Asprovided above, the responses to these questions are stored in thepatient preferences table 410 as ratings 412 of 0 (i.e., “no”) or 1(i.e., “yes”).

With continued reference to the patient preferences screen 610, the HCPmay be presented with one or more advisory notes 612 including datarelevant to determining and selecting treatment guideline ratings 412for the patient. For example, the advisory notes 612 may include a firstadvisory note 612 a displaying a calculated glucose (BG) ratio forconsideration when determining whether the patient requires additionalmealtime coverage. The BG ratio 612 a is calculated by taking a mean ofall BG measurements taken during lunch (BG_(Lunch)), dinner(BG_(Dinner)), and bedtime (BG_(Bedtime)) intervals, over a mean of allBG measurements taken during a fasting interval prior to breakfast(BG_(Breakfast)). For instance, the BG ratio 612 a may be expressed bythe following formula:

$\begin{matrix}{{{BR}{Ratio}} = \frac{{Average}\left( {{BG}_{Lunch},{BG}_{Dinner},{BG}_{Bedtime}} \right)}{{Average}\left( {BG}_{Breakfast} \right)}} & (1)\end{matrix}$

Additional concepts and features related to average BG measurements foreach of the BG intervals can be found in U.S. Patent ApplicationPublication No. 2017/0228518, the disclosure of which is incorporatedherein in its entirety. A BG ratio 612 a greater than 1.00 indicatesthat the average meal-related BG measurements (BG_(Lunch), BG_(Dinner),BG_(Bedtime)) are higher than the average fasting BG measurements takenbefore breakfast (BG_(Breakfast)). Conversely, for BG Ratios less thanor equal to 1.00, the HCP may identify the patient as not requiringadditional mealtime coverage. Accordingly, an advisory note showing theBG ratio 612 a is provided to the HCP in the patient preferences inputscreen 610 so that the HCP may identify the patient as needingadditional mealtime coverage.

Referring still to the patient preferences screen 610 of FIG. 6A, theHCP may also be presented with an advisory note 612 indicating atreatment compliance rate 612 b for the patient 10, which can beconsidered by the HCP 40 in determining whether the patient 10 iscapable of handling complex treatment regimens. The system calculatesthe treatment compliance rate based on information obtained from thepatient device data table 450 shown in FIG. 4E. For example, as shown inFIG. 4E, the patient 10 may be associated with a smart pill bottle(eBottle_Rx) 123 c capable of tracking each instance of the bottle 123 cbeing opened (e.g., the bottle access data of FIG. 1D). The number ofbottle openings (Bottle_Openings_wk) is then stored in the device datatable 450. Treatment compliance rate 612, 612 b is then calculated as aratio of the number of measured bottled openings per week over thescheduled doses per week by the following formula:

$\begin{matrix}{{{Treatment}{Compliance}} = \frac{{Measured}{Bottle}{Openings}}{{Scheduled}{Doses}}} & (2)\end{matrix}$

If more than one medication is currently prescribed to the patient 10,the treatment compliance rate 612, 612 b may be calculated as an averageof the treatment compliance rate for each one of the prescribedmedications.

Referring to FIG. 4B, an allergies and exclusions table 420 includes alisting of all ADMs that a patient is either allergic to or that havebeen excluded from the treatment regimen for other reasons. For example,ADMs may be excluded by the patient or HCP 40 based on the undesirableside-effects or contraindications. The allergies and exclusions table420 is in reciprocal communication with an allergies and conditionsscreen 620 (FIG. 6B). Here, the data included in the allergies andexclusions table 420 is presented to the patient 10 or HCP 40 in theallergies and conditions screen 620 on the display 116, 146. Theallergies and exclusions table 420 may update based on feedback receivedfrom inputs to the allergies and conditions screen 620 by the patient 10or HCP 40. This interactive relationship is described in greater detailbelow.

FIG. 4C illustrates an example of a current medications table 430including a listing of all medications currently being taken by thepatient 10. The current medications table 430 may also be referred to asa current medications list 430. As shown in rows 2 and 3 of theillustrated current medications table 430, non-ADM medications may alsobe included in the current medications table 430. The currentmedications table 430 is queried by the program 160 as part ofdetermining potentially adverse interactions between suggested treatmentregimens and medications currently taken by the patient 10. Further,once a treatment regimen is selected and implemented, the ADM selectionprogram 160 may update the current medications table 430 to includechanges or additions to the listed medications.

Referring to FIGS. 2, 4D and 4E, the data storage 200 further includes apatient device calibration table 440 and the patient device data table450 discussed above. The patient device data table 450 may be providedas a linked child (FIG. 2 ) to the patient device calibration table 440,whereby the patient device data table 450 is used by the system 100 tomaintain calibration of each of the devices. For example, the patientmay use a fitness tracker 110 c, a smart phone 110 b, a BG monitor 124,a smart pill bottle 123 c, and a smart scale 125 all listed by thepatient device calibration table 440 and the patient device data table450. The data for each of the devices 110 c, 110 b, 124, 123 c, 125 inthe patient device data table 450 is communicated to the system 100 fromeach device 110 c, 110 b, 124, 123 c, 125. Accordingly, the patientdevice data table 450 may be updated in real-time, at regular intervals,or on-demand.

Based on the data provided in the patient device data table 450, each ofthe devices 110 c, 110 b, 124, 123 c, 125 can be calibrated. Forexample, the parameter of Calories-per-Mile-by-GPS can be calibrated bytaking the actual calories burned by GPS for the previous week dividedby the actual miles by GPS for the previous week. For instance, theCalories-per-Mile-by-GPS can be calculated by the following formula:

$\begin{matrix}{{{Calories\_ per}{\_ Mile}{\_ by}{\_ GPS}} = \frac{{Calores\_ by}{\_ GPS}{\_ wk}}{{Miles\_ by}{\_ GPS}{\_ wk}}} & (3)\end{matrix}$

The calculated value of this calibration constant, (Calories-per-Mile byGPS), is stored in the patient device calibration table 440. Anotherexample is (Calories_per_rep_per-Lb_WeightMachine_A), which also isdependent on a resistance weight machine's weight load, in Lb. Forinstance, the Calories_per_rep_per−Lb_WeightMachine_A can be calculatedusing the following formula:

$\begin{matrix}{{{Calories\_ per}{\_ rep}{\_ per}{\_ lb}{\_ WeightMachine}{\_ A}} = \frac{{Calories\_ by}{\_ WeightMachine}{\_ A}{\_ wk}}{\left( \frac{{Reps\_ by}{\_ WeightMachine}{\_ A}{\_ wk}}{{WeightMachine\_ A}{\_ weightload}} \right)}} & (4)\end{matrix}$

The calibration ratios are considered permanent but may be re-calculatedand re-saved with each therapy update. The ratio enables the HCP 40 toprescribe exercise with knowledge of the calories it will burn.

FIG. 4F shows a current conditions table 460 linked as a child to thepatient preferences table 410 (FIG. 2 ) and populated based oninformation provided from a current labs table 470 (FIG. 4G),contraindications table 550 (FIG. 5E), and the allergies and conditionsscreen 620 (FIG. 6B). More specifically, the current conditions table460 is populated by comparing each of the records (i.e., lab results) ofthe current labs table 470 against each of the records of thecontraindications table 550 to identify commonality. If one of the labresults listed in the current labs table 470 satisfies one of thecontraindicating conditions listed in the contraindications table 550,then the dosing controller 160 identifies the corresponding conditionfor input to the current conditions table 460. For example, the currentlabs table 470 shown in FIG. 4G shows a Glomerular Filtration Rate (GFR)measurement of 55%, which is shown in row 1 of the contraindicatingconditions table 550 of FIG. 5E as a resulting contraindicatingcondition. Accordingly, the contraindicating condition is listed in thecurrent conditions table 460. The current conditions table 460 may befurther populated based on responses provided by the HCP 40 in theallergies and conditions screen 620 of FIG. 6B. For example, theallergies and conditions screen 620 may include fields for enteringcurrent conditions and side effects of the patient 10.

The current conditions table 460 serves two purposes: first, to resolveconflicts between the inputs from the allergies and conditions screen620 and the current labs table 470; and second, to provide for therecording and storing of the conditions of the patient 10 on the date ofthe update. Accordingly, the current conditions table 460 is provided asan interactive screen, whereby the resolution of conflicts isaccomplished by a process of verification or concurrence, which is doneby the HCP 40 using corresponding graphical radio buttons 462 providedin the HCP Assessment Positive column. The current conditions table 460allows the HCP 40 to view the conditions along with the applicable labresults and make a judgment-based decision about the condition. Theconditions that are fed into the current conditions table 460 from theallergies and conditions screen 620 are automatically filled with thevalues from the allergies and conditions screen 620.

Referring to FIG. 5A, the ADM table 510 includes a schedule of allavailable ADMs, which are indexed to be linked to a plurality ofsubtables 520, 530, 550 (FIG. 5E), 560 (FIG. 5F), as described ingreater detail below. The ADM table 510 is populated with prescribingdrug information 512 and scaled guidelines 514 derived from thereferences 196, 198 discussed above. Drug information 512 may include aFood and Drug Administration National Drug Code (FDA-NDC) number 512 a,an ADM classification 512 b, a generic name 512 c, and a delivery method512 d. The ADM table 510 is also populated with respective scaledguidelines 514, 514 a-f for each of the ADMs.

The scaled guidelines 514, 514 a-f in the illustrated ADM table 510include, but are not limited to, guidelines 562, 562 a-f shown in thetable entitled guideline refreshment conversion table 560 (FIG. 5F). Theguidelines 562 in the illustrated example of the guideline refreshmentconversion table 560 include efficacy 562 a, hyopglycemia risk 562 b,effect on body weight 562 c, cost 562 d, complexity 562 e, and mealtimecoverage 562 f. Efficacy 562 a describes how well the ADM reducesglucose concentration and hemoglobin A1c. Hypoglycemia risk 562 b is theprobability that the ADM will cause hypoglycemia. Weight effect 562 c isthe effect of the ADM on patient's weight, ranging from weight-loss atthe lower end of the parameter's range to weight-gain at the upper end.Cost 562 d corresponds to the dollar-cost of the ADM. Complexity 562 erelates to the amount of trouble and inconvenience incurred by a patienttaking the ADM. Meal coverage 562 f is the degree to which an ADM ismore active at meals.

Several of the guidelines 562 are provided by the references 198 inscaled form (e.g. Low, Medium, High). However, the guidelines 562 aretranslated to number scaled guideline values 514 between 0 and 1 inaccordance with the guidance in the tabulated guideline refreshmentconversion process table 560 (FIG. 5F). These numeric scaled guidelinesvalues 514 are given names such as Scaled Hypo Risk, andScaled_Weight_Effect. These scaled guideline values 514 are sent to theADM Table 510 for storage. The ADM table 510 may occasionally berefreshed or updated to reflect revisions to the scaled guideline values514 based on changes to the guidelines 562 in the guideline refreshmentconversion table 560.

The principal of the ADM selection system 100 is to assess theapplicability of each available ADM to the health status of the patient10 based on several criteria, including patient preferences, patientmedical conditions, published treatment guidelines, and availability ofalternative treatment regimens, for example. An example of an ADMselection table 800 is provided in FIG. 8 for the purpose ofillustrating an implementation of the ADM selection system 100. However,in practice the ADM selection system 100 may determine recommended ADMs810 without the use of the ADM selection table 800.

Referring to FIG. 7 , in some implementations, the dosing controller 160executes an ADM selection process 700 to select available ADMs 810 forthe treatment regimen of a patient 10. The ADM selection process 700includes a first step 710 of populating an ADM selection table 800 (FIG.8 ) with a listing of available ADMs 810, 810 a-i, which are obtainedfrom the ADM table 510. The ADM selection table 800 of FIG. 8 shows eachADM 810 associated with one or more demerit values 812, including anadverse demerit value 812 a, an instruction demerit value 812 b, a guidedemerit value 812 c, a modified demerit value 812 d, and a total demeritvalue 812 e. While available ADMs 810 a-i are shown, the ADM selectiontable 800 may include more or less ADMs 810, including different typesof ADMs 810 presently available or that may become available in thefuture for managing glucose levels. Although represented as a table 800in the example shown, the list of available ADMs 810 may be implementedin any format. In some instances, the ADM selection table 800 isprefilled from prior iterations of the ADM selection process 700. Insuch cases, the first step 710 of the ADM selection process 700 includesan initialization step, whereby each of the demerit values 812 is“zeroed” and the dose notes are cleared. Each of the ADMs 810 in the ADMselection table 800 may also be associated with one or more dose notes814, 814 a-b assigned by the ADM selection process 700.

The ADM selection process 700 calculates the demerit values 812 usingpredetermined increment values 572 obtained from the configurableconstants table 570 (FIG. 5G). As shown in FIG. 5G, the configurableconstants table 570 includes an adverse demerit increment value 572 a,an instruction demerit increment value 572 b, and a guideline demeritvalue increment value 572 c, along with other configurable constants,which are discussed further below. The increment values 572 forcalculating each of the demerit values 812 a-812 c can be modified inthe configurable constants table 570 by the HCP depending on a desiredweight to be given to each type of demerit. In the illustrated example,the adverse demerit increment value 572 a is larger than the otherdemerit increment values 572 b, 572 c. The adverse demerit incrementvalue 572 a is used for the steps of checking for adverse interactionsbetween drugs in the ADM selection table 800 and the drug interactionstable 520 (FIG. 5B), and in the step for checking for contraindicatingconditions associated with each of the ADMs 810. These two steps areconsidered highly important and, accordingly, are configured to confermore demerits than other processes. By assigning a high value to theadverse demerits increment value 572 a, ADMs 810 that are identified ashaving adverse interactions or contraindicating conditions are lesslikely to be recommended by the ADM selection system 100. In theillustrated example, the adverse demerit increment value 572 a isassigned a value of 60 demerits in the configurable constants table 570.This compares with the illustrated value of 10 demerits for guidelinedemerits. In the current example, there are six guidelines 412 a-412 f(see patient preferences table 410). Accordingly, if each receives amaximum value of 10, the total guideline demerit value 812 c would equal60 demerits, which equals the total demerits of an ADM having oneadverse interaction or contraindicating condition. The result of thistiered system of demerit increments 572 is that the contraindicatingconditions and adverse interactions provide a coarse evaluation of theADM under consideration and the guidelines provide a fine evaluation.

Referring back to FIG. 7 , once the ADM selection table 800 is populatedand initialized, a second step 720 of the ADM selection process 700includes assigning the modified demerit values 812 d for each of theADMs 810. Here, the ADM selection process 700 queries 722 the currentmedications table 430 (FIG. 4C) for each ADM 810 listed in the ADMselection table 800. If an ADM 810 is included in the currentmedications table 430, the ADM selection process 700 assigns 723 thecorresponding ADM 810 a negative (low) modified demerit value 812 d,such as −200, for example. The assigning of a negative (low) modifieddemerit value 812 d ensures that the corresponding ADM 810 will have alow total modified demerit value 812 d, which will, in turn, ensure thatthe corresponding ADM 810 will be included among the most suitable ADMs810 for selection from the list. In addition to adjusting the modifieddemerit value 812 d, the ADM selection process 700 may also edit a firstdose note 814 a to indicate that the corresponding ADM 810 will beselected as part of the current treatment regimen for the patient 10.

The second step 720 of the ADM selection process 700 further queries 724the allergies and exclusions table 420 (FIG. 4B) for each of the ADMs810, 810 a-810 i in the ADM selection table 800. If an ADM 810, 810a-810 i is listed within the allergies and exclusions table 420, thenthe ADM selection process 700 assigns 725 the modified demerits value812 d with a relatively high value (e.g. 200 demerits). By contrast toassigning a relatively low (e.g., negative) value (e.g., −200 demerits),a relatively high value for the demerits associated with the inclusionin the allergies and exclusions table 420 ensures that the correspondingADM 810 will be ranked low on the list.

A third step 730 of the ADM selection process 700 includes incrementingadverse and/or instruction demerit values 812 a, 812 b for each of theADMs 810. Here, the ADM selection process 700 queries 732 thecontraindications table 550 (FIG. 5E) for each ADM 810 and the currentconditions table 460 (FIG. 4F) to determine whether any contraindicatingconditions listed in the contraindications table 550 are present in thecurrent conditions table 460 for the patient 10. If a contraindicatingcondition associated with an ADM 810 is listed in the current conditionstable 460, the corresponding graphical radio button 462 in the currentconditions table 460 is selected, and if there are not any specialdosing instructions associated with the ADM 810, then the ADM selectionprocess 700 increments 733 the adverse demerit value 812 a of the ADM by60 demerits. On the other hand, if the corresponding ADM 810 listed inthe current conditions table 460 does include special dosinginstructions, then the ADM selection process 700 increments 735 theinstruction demerit value 812 b by 30 demerits and adds a correspondingnote indicating “conditional dosing” to the dosing notes 814 (FIG. 8 ).

The third step 730 of the ADM selection process 700 also queries 734 thedrug interactions table 520 (FIG. 5B) for each ADM 810 listed in the ADMselection table 800 to determine if any of the medications in the ADMselection table 800 interact with any of the medications that are partof the current treatment regimen. If a first ADM 810 in the selectiontable 800 has an adverse interaction with a second ADM 810, and thesecond ADM 810 is listed in the current medications table 410, the ADMselection process 700 increments 737 the adverse demerit value 812 a forthe first ADM by 60 demerits.

In some examples, the third step 730 of the ADM selection process 700also queries 736 the permanent conditions table 320 (FIG. 3B) and thecontraindications table 550 (FIG. 5E). The contraindicating conditionsfor each of the ADMs 810 in the ADM selection table 800 are comparedwith the permanent conditions listed in the permanent conditions table320. If a permanent condition is included in the contraindications table550 for the corresponding ADM and the condition appears with HCPconcurrence in the current conditions table 460 (FIG. 4D), then the ADMselection process 700 increments 739 the adverse demerits value 812 afor the corresponding ADM 810 by 60 demerits.

The third step 730 of the ADM selection process 700 may further assign738 the guideline demerit value 812 c for each ADM 810 in the ADMselection table 800. The assigning of the guideline demerit value 812 cincludes querying 738 a each of the patient preferences table 410, theADM table 510, and the configurable constants table 570 to obtain thetreatment guideline rating values 412, 412 a-f, the scaled guidelinevalues 514, 514 a-f, and a configurable guideline demerit incrementvalue 572 c for the corresponding ADM 810. The ADM selection process 700may calculate 738 b the guideline demerit value 812 c by multiplyingeach of the scaled guideline values 514 by the corresponding treatmentguideline rating value 412 and by the guideline demerit increment value572 c (i.e., 10) from the configurable constants table 570 for all ofthe guidelines listed. Accordingly, the guideline demerit value 812 cfor each ADM is the sum of the calculated demerit values for each of theguidelines, as provided in the following equation:

Value_(GuidelineDemerit)=Σ(Value_(Scaled)(Guideline)*Value_(Importance)(Guideline)*10)  (5)

Once ADM selection process 700 assigns the corresponding guidelinedemerit values 812 c for each ADM 810, a fourth step 740 of the ADMselection process 700 calculates the total demerit value 812 e bysumming the adverse demerit value 812 a, the instruction demerit value812 b, and the guideline demerit value 812 c for the respective ADM.Additionally, in instances where an ADM 810 does not have a modifieddemerit value 812 d, the total demerit value 812 e will also be used asthe modified demerit value 812 d. Similarly, ADMs having an assignedhigh (e.g., positive) modified demerit value 812 d (e.g., 200) mayreplace the corresponding total demerit value 812 e.

In some implementations, a fifth step 750 of the ADM selection process700 filters and sorts the ADMs 810 in the ADM selection table 800 basedon the total demerit values 812 e calculated during the fourth step 740.In some examples, the fifth step 750 of the ADM selection process 700initially sorts 752 the ADM selection table 800 based on the totaldemerit values 812 e and the modified demerit values 812 d. Here, theinitial sorting 752 orders total demerit values 812 e for the ADMs 810from lowest to highest. In some examples, any ADM 810 having acorresponding low (e.g., negative) modified demerit value 812 d assignedduring the second step 720 may be added to the ordered list to appear atthe lowest position. For example, the ADM 810 included in the currentmedications table 430 (FIG. 4C) that was assigned a modified demeritvalue of −200, as discussed above, would appear at the top of the sortedADM selection table 800. In some examples, an ADM having an assignedhigh (e.g., positive) modified demerit value 812 d replaces thecorresponding total demerit value 812 e to ensure that the correspondingADM 810 is ordered at the highest position. For instance, the ADM in theallergies and exclusions table that was assigned a modified demeritvalue of 200 would appear at the bottom of the sorted ADM selectiontable 800.

In lieu of the initial sorting 752 from lowest to highest based on thetotal demerit values 812 e or the modified demerit values 812 d (whenapplicable), the fifth step 750 of the ADM selection process 700 mayoptionally execute two sorting steps 753, 753 a-b. The first sortingstep 753 a includes filtering out each ADM 810 from the ADM selectiontable 800 that includes a corresponding total demerit value 812 e thatsatisfies (e.g., greater than or equal to) a demerit threshold value. Asused herein, “filtering out” refers to removing an ADM 810 from the ADMselection table 800 so that the corresponding ADM 810 will not beselected as part of the treatment regimen for the patient 10. In someexamples, the demerit threshold value is equal to 60 demerits and issatisfied when the total demerit value 812 e is greater than or equal to60 demerits threshold. Thus, the demerit threshold value may be selectedto filter out any ADMs having contraindicating conditions listed in thecontraindications table 550 that are also present in the currentconditions table 460 and/or the permanent conditions table 320 for thepatient 10 and/or to filter out any ADMs that interact (e.g., byaccessing the drug interactions table 520) with medications the patient10 is currently taking (e.g., by accessing the current medications table430). The second sorting step 753 b includes sorting the remaining ADMs810 (i.e. ADMs having a total demerit value 812 e less than or equal to60 demerits) from low-to-high based on their respective guidelinedemerit values 812 c. Accordingly, the optional sorting steps 753 sortthe ADMs 810 in the ADM selection table 800 from lowest to highest basedon the guideline demerit values 812 c after filtering out (e.g.,removing) all ADMs associated with corresponding total demerit values812 e satisfying the demerit threshold value.

With the ADM selection table 800 sorted via the initial sorting 752based on the total demerit values 812 e and/or assigned modified demeritvalues 812 d, or the optional sorting steps 753 based on the guidelinedemerit values 812 c after filtering out any ADMs associated withcorresponding total demerit values 812 e satisfying the demeritthreshold value, the fifth step 750 of the ADM selection process 700selects 754 a predetermined number of recommended ADMs 810 having thelowest total demerit values 812 e or lowest guideline demerit values 812from the sorted ADM selection table 800 for display on the display 146associated with the HCP 40. The HCP 40 may view the predetermined numberof recommended ADMs 810 to determine whether or not some or all shouldbe included in the treatment regimen for the patient 10. Thepredetermined number of ADMs 810 selected may be set by the N-Finalistsconstant 574 (e.g., “3”) in the configurable constants table 570 (FIG.5G). Here, the number of ADMs 810 recommended by the ADM selectionprocess 700 is in addition to any ADMs 810 that the patient 10 iscurrently taking (e.g., included in the current medications table 430).For instance, an ADM 810 included in the current medications table 430may have a modified demerit value 812 d equal to −200, while the nextlowest-scoring ADMs 810 not included in the current medications table430 may have total demerit values 812 e equal to “10”, “20”, and “30”,respectively. Thus, if N_Finalists is configured to a value of 3, thenall four of these ADMs will be displayed in the ADM selection table 800as recommended ADMs for inclusion in the treatment regimen of thepatient 10. In this way, the HCP 40 will be able to see any ADMs thepatient 10 is currently taking even if these ADMs would not have beenone of predetermined number of ADMs 810 selected from the sorted ADMselection table 800 based on the initial sorting 752 or the sortingsteps 753.

Once the recommended ADMs 810 are identified, the ADM selection process700 executes a dosage step 760 to determine/calculate a dosage for eachof the recommended ADMs 810 based on a comparison between a target A1cvalue (Target_A1c) 411 b and an energy-adjusted A1c value(Energy-Adjusted_A1c) 611. The target A1c value 411 b is obtained fromthe patient preferences table 410 (FIG. 4A) and the energy-adjusted A1cvalue 611 for the patient 10 is calculated using Equation 11 below.

Referring to FIG. 6C, an energy-based dosage screen 630 determines theenergy-adjusted A1c value 611 by adjusting a current A1c value 632 basedon fitness-related data received from the patient devices 110. Thecurrent A1c value 632 may be obtained from the current labs table 470(FIG. 4G) and converted to a BG value (eBG) by a function subroutinethat contains a published correlation as follows:

eBG=eBG[FUNCTION(A1c)]  (6)

The eBG may then be converted to a value of excess carbohydrate gramsper day (Carbs_XS) as follows:

Carbs_XS=(eBG−TargetBG)*HTF[FUNCTION(Weight)]  (7)

where HTF is a hypoglycemia treatment factor based on a weight of thepatient 10. If the patient has a linked scale device 125, then theweight (eWeight) obtained from the smart scale 125 is substituted forclinic-measured weight throughout the program.

The excess carbohydrate grams per day (Carbs_XS) may be converted toexcess energy (Calories_XS) 634 by multiplying by a Calories_Per_Carbconstant 576 (e.g., 4) provided in the configurable constants table 570(FIG. 5G). The parameter for remaining energy surplus value(Remaining_Calories_XS) 636 is initialized to the excess energy value.

The HCP 40 uses the energy-based dosing screen 630 of FIG. 6C to providean energy-based dose adjustment for the patient 10. Here, theenergy-based dose adjustment may change a dosing for each recommendedADM based on an exercise regimen for the patient 10. The exerciseregimen may be obtained by tracking exercise data from the patientdevices 110. The tracked exercise data may be used to determine afrequency, intensity, duration, and types of exercises associated withthe patient's 10 exercise regimen. In some examples, dosing prescribedto a patient is reduced when the patient is more active. The remainingenergy surplus value (Remaining_Calories_XS) 636 is adjusted bysuccessive changes to the exercise regimen and dietary carb intake asentered by the HCP 40. This process involves a deliberatetrial-and-error process, which is done interactively, preferably whilethe HCP 40 and the patient 10 are communicating with one another. Thisinsures that the HCP 40 does not prescribe an exercise regimen that thepatient is unwilling to comply with. Several forms of exercise may beprescribed. Also one or more of the patient devices 110 may be equippedor connected to a carbohydrate-counting database. This enables the HCP40 to prescribe changes to the carbohydrate count in the patient's 10diet. The decrement to the remaining energy surplus value(Remaining_Calories_XS) is tallied in the same manner as for exercisechanges.

In the example shown, the HCP 40 uses the energy-based dosing screen 630to change the exercise regimen for the patient 10 by adjusting use ofWeight Machine A 635. The machine's weight load(WeightMachine_A_Weight_Load) is entered in the “load or NA” entry box.The current average value of the reps per week is obtained from thepatient device data table 450 (FIG. 4E) and the calibration constant(Calories_per_rep_per_Lb_WeightMachineA) is obtained from the patientdevice calibration table 440 (FIG. 4D). The change in exercise(Recom_Change−WeightMachine_A_reps) is input by the HCP 40. Theresulting change is usually a decrement to the patient's remainingexcess calories, but just in case, the sign is accounted-for. Theresulting change to the remaining energy surplus value (Calories dRxWMA) is calculated using the calibration constant as follows:

Calories_dRx_WMA=(Recom_Change−WeightMachine_A_reps)*(Calories−per-rep-per-Lb_WeightMachine_A)*(WeightMachine_A_WgtLoad)  (8)

The decremented remaining energy surplus value (Remaining_Calories_XS)incorporating all decrements is converted back to an A1c value aftereach successive decrement, so that the HCP 40 can see what the predictedA1c will be. The predicted value of A1c is called the energy-adjustedA1c value (Energy_Adjusted_A1c) 611. The conversion is accomplished bythe formulas below:

$\begin{matrix}{{Carbs\_ Changed} = \frac{{Calories\_ XS} - {RemainingCalorie\_ XS}}{{Calories\_ per}{\_ Carb}}} & (9)\end{matrix}$ $\begin{matrix}{{eBG\_ Changed} = \frac{Carbs\_ Changed}{{HTF}\left\lbrack {{Function}({Weight})} \right.}} & (10)\end{matrix}$ $\begin{matrix}{{{Energy} - {{Adjusted\_ A}1C}} = {{eA}1{c\left\lbrack {{FUNCTION}\left( {{eBG} + {eBG\_ Changed}} \right)} \right.}}} & (11)\end{matrix}$

While the example above adjusts the weight load for Weight Machine A 635for adjusting the exercise regimen for the patient 10, other exerciseregiments may not require changes in load. When the HCP 40 is satisfiedwith the results shown in energy-based dosage screen 630, he/she exitsthe screen 630 and proceeds with the patient's update process. Thescreen and status of the parameters remain as-is, so that the HCP 40 canreturn to the screen, if desired 630. The latest calculatedenergy-adjusted A1c value (Energy_Adjusted_A1c) 611 is used by thedosage step 760 of the ADM selection process 700 fordetermining/calculating the dosage for each of the recommended ADMs 810so that the energy-based A1c adjustments are accounted for in the dosecalculations. For each recommended ADM 810, the dosage step 760 furthercompares a sum of a current dose value (Current_Dose) and a startingdose value (Start Dose) with a maximum allowable dose (Max_Dose). TheCurrent_Dose may be obtained from the current medications table 430(FIG. 4C) and the Start Dose and the Max_Dose may be obtained from theADM table 510 (FIG. 5A).

If an ADM is included in the current medications table 430, theenergy-adjusted A1c value 611 is greater than the target A1c value, andthe sum of the current dosage value and the start dosage value for theADM is greater than the maximum dosage value, then the system recommendsthe current dosage value for the ADM and provides a prompt (i.e. note)to maintain the current dosage value of the ADM and to add another ADM.If the sum of the current dosage value and the start dosage value isless than or equal to the maximum dosage value and if the dosage notesare null, then the recommended dosage value is the sum of the currentdosage value and the start dosage value. However, if the dosage notesare not null, such as when special dosing instructions are identifiedfor an ADM, then the system 100 provides a prompt (i.e. note) for theHTC to consult manufacturer dosing instructions for all ADMs, except formetformin. In the case of metformin, the system 100 recommendsmaintaining the current dosage value and adding another ADM. In caseswhere the ADM is listed in the current medications table 430 and theenergy-adjusted A1c value is less than or equal to the target A1c value,the system 100 recommends the current dosage for the ADM, and provides aprompt (i.e. note) recommending no change in dosage.

Once the ADM selection process 700 determines the recommended dosagevalues for each recommended ADM during the dosage step 760, the processexecutes a cost step 770 to calculate a total cost of the suggestedrecommended therapy based on the cost per dose and the total dosagevalues recommended for each recommended ADM 810. Thus, the cost step 770may determine a cost for each recommended ADM 810 by multiplying thecost per dose times the total dosage value recommended and then sum thecosts of all the recommended ADMs 810 to determine the total cost of thesuggested recommended therapy. Thereafter, the ADM selection process 700executes a selection screen step 780 for generating an ADM selectionscreen 640 (FIG. 6D) based on the total cost of the suggestedrecommended therapy calculated during the cost step 770.

Referring to FIG. 6D, the ADM selection screen 640 graphically displaysa representation of the ADM selection table 800 on the display 116, 146.In the example shown, the ADM selection screen 640 includes energy-basedtreatment information 642 and a listing 644 of the recommended ADMs 810,810 a-c. In the example shown, the listing 644 includes a firstrecommended ADM 810 a of Jardiance (empagliflozon), a second recommendedADM 810 b of Invokana (canegliflozin), and a third recommended ADM 810 cof Lantus (glargine U-100). Each recommended ADM 810 of the listing 644on the screen 640 includes an associated recommended dosage value 646,ADM notes 647 (i.e. side-effects, dosages, adverse interactions), and afitness level 648 indicating how well a particular ADM matches thepatient 10. The HCP 40 may edit the ADM selection screen 640 to makechanges to the recommended ADMs. For example, the HCP may adjust one ormore of the recommend dosage values 646. The ADM selection screen 640may also include a button 649 for opening the ADM selection table 800.Thus, the HCP 40 may select the button 649 to access the ADM selectiontable 800 when the HCP 40 wants to view and/or select an ADM that wasnot included in the recommended ADMs on the ADM selection screen 640.

Once the HCP 40 is satisfied with the recommended ADMs, the HCP 40 maysave the recommended therapy regimen. Referring back to FIG. 7 , the ADMselection process 700 executes a transmission step 790 to transmit therecommended therapy regimen to the patient 10. The process 700 maytransmit the recommended therapy regimen to the patient 10 via at leastone of a text message (SMS), electronic mail, a pre-recorded telephonemessage, a printed report, a web-based application, or by a downloadableapplication, for example. The dosing controller 160 may route therecommended therapy regimen to one or more of the patient devices 110.Using the recommended therapy regimen, the smart pill bottle 123 ccontaining one of the recommended ADMs 810 may alert the patient 10 whenthe regimen specifies it is time for the patient 10 to administer theADM 810. For instance, the bottle 123 c may include a display thatpresents the appropriate dosage for the patient 10 to administer. Thebottle 123 c may also unlock when it is time for the patient 10 toadminister the ADM 810. Similarly, when the recommended ADM 810 includesinsulin (e.g., basal insulin such as Lantus), the dosing controller 160may send a recommended dosage to the pen 123 b that causes the pen 123 bto automatically dial in a number of units associated with therecommended dosage and administer the recommended dosage to the patient10.

Referring to FIG. 9 , a method 900 of selecting a diabetes treatmentregimen includes obtaining 902, at data processing hardware 112, 132,142, prescribing drug information and published guidelines for each of aplurality of Anti-Diabetes Medications (ADMs) 810 available for managingglucose levels. The ADMs may be used to manage glucose levels inoutpatients having Type 2 Diabetes or for those who are at risk ofdeveloping Diabetes. The method 900 includes the data processinghardware 112, 132, 142 receiving 904 patient information associated witha patient 10 seeking selection and dosing of one or more of theavailable ADMs 810.

For each available ADM, the method 900 includes the data processinghardware 112, 132, 142 determining 906 an adverse demerit value 812 a,an instruction demerit value 812 b, and a guideline demerit value 812 cbased on the patient information and the prescribing drug information196 and published guidelines 198 for the corresponding ADM 810, anddetermining 908 a total demerit value 812 e by summing the adversedemerit value 812 a, the instruction demerit value 812 b, and theguideline demerit value 812 c. The method 900 also includes the dataprocessing hardware 112, 132, 142 ordering 910 the total demerit values812 e for the available ADMs 810 from lowest to highest and selecting apredetermined number of recommended ADMs associated with the lowesttotal demerit values 812 e.

The method 900 also includes the data processing hardware 112, 132, 142determining 912 a recommended dosage for each recommended ADM 810 andtransmitting a therapy regimen to a patient device associated with thepatient, the therapy regimen including the recommended ADMs 810 and therecommended dosage for each recommended ADM 810.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Moreover,subject matter described in this specification can be implemented as oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, data processing apparatus. Thecomputer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter affecting a machine-readable propagated signal, or a combinationof one or more of them. The terms “data processing apparatus”,“computing device” and “computing processor” encompass all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as an application, program, software,software application, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, one or more aspects of thedisclosure can be implemented on a computer having a display device,e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, ortouch screen for displaying information to the user and optionally akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

One or more aspects of the disclosure can be implemented in a computingsystem that includes a backend component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a frontend component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or any combination of one or more such backend,middleware, or frontend components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multi-tasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. Accordingly, otherimplementations are within the scope of the following claims. Forexample, the actions recited in the claims can be performed in adifferent order and still achieve desirable results.

What is claimed is:
 1. A computer-implemented method executed on dataprocessing hardware that causes the data processing hardware to performoperations comprising: receiving patient information associated with apatient seeking selection and dosing of one or more of the availableADMs, the patient information comprising: a current medications listincluding a list of medications and corresponding dosages the patient iscurrently prescribed; and medical conditions associated with thepatient; obtaining treatment guideline ratings each assigned by thepatient that measures a subjective level of importance to the patientfor a corresponding treatment guideline, the treatment guideline ratingsincluding at least one of a cost rating, a body weight rating, atreatment regimen complexity rating, a treatment efficacy rating, amealtime coverage needs rating, or a hypoglycemia rating; for eachcorresponding available ADM of the plurality of available ADMs:obtaining scaled guideline values for the corresponding available ADMbased on the prescribing drug information and the published guidelines,each scaled guideline value associated with a corresponding treatmentguideline rating; determining a guideline demerit numerical value forthe corresponding available ADM by multiplying, for each treatmentguideline rating, the treatment guideline rating times the correspondingscaled guideline value and a guideline demerit increment value; anddetermining a total demerit value for the corresponding available ADMbased on the guideline demerit numerical value determined for thecorresponding available ADM; ordering the total demerit numerical valuesin numerical order for the available ADMs from the lowest total demeritnumerical value to the highest total demerit numerical value; selectinga predetermined number of recommended ADMs associated with the lowesttotal numerical demerit values from the plurality of available ADMs;determining a recommended dosage for each recommended ADM based on thepatient information, the prescribing drug information, and the publishedguidelines; and transmitting, via a network, instructions to anadministration device associated with one of the recommended ADMs, theinstructions when received by the administration device causing theadministration device to configure a doser of the administration deviceto administer the recommended dosage of the one of the recommended ADMsto the patient.
 2. The computer-implemented method of claim 1, whereinthe patient information further comprises at least one of: a targetglucose range for the patient; a target A1c value for the patient; apreferred minimum monthly treatment cost; or a preferred maximum monthlytreatment cost.
 3. The computer-implemented method of claim 1, whereinthe patient information further comprises treatment guideline ratingseach assigned by the patient that measures a subjective level ofimportance to the patient for a corresponding treatment guideline, thetreatment guideline ratings comprising at least one of a cost rating, abody weight rating, a treatment regimen complexity rating, a treatmentefficacy rating, a mealtime coverage needs rating, or a hypoglycemiarating.
 4. The computer-implemented method of claim 1, wherein thepatient information further comprises at least one of: one or moreglucose values for the patient measured by a glucometer or continuousglucose monitor in communication with the data processing hardware; oran A1c value for the patient.
 5. The computer-implemented method ofclaim 1, wherein the operations further comprise: receiving exercisedata from a fitness tracker associated with the patient; and adjustingthe recommended dosage for at least one of the recommended ADMs based onthe received exercise data.
 6. The computer-implemented method of claim1, wherein the operations further comprise: obtaining prescribing druginformation and published guidelines for each of a plurality ofavailable Anti-Diabetes Medications (ADMs) for managing glucose levels,the prescribing drug information and published guidelines comprising,for each corresponding available ADM of the plurality of available ADMs:one or more contraindicating conditions associated with thecorresponding available ADM; and a list of medications that interactwith the corresponding available ADM; and for each correspondingavailable ADM of the plurality of available ADMs: assigning an adversedemerit increment value when the medical conditions associated with thepatient indicate that the patient currently has any of thecontraindicating conditions associated with the corresponding availableADM; and assigning the adverse demerit increment value when the currentmedications list indicate that the patient is currently taking at leastone of the medications that interact with corresponding available ADM;wherein determining the total demerit numerical value for thecorresponding available ADM is further based on a sum of each adversedemerit increment value assigned to the corresponding available ADM. 7.The computer-implemented method of claim 1, wherein the operationsfurther comprise: obtaining a list of excluded ADMs that the patient iseither allergic to or is excluded from the treatment regimen for thepatient; and for at least one corresponding available ADM of theplurality of available ADMs: determining the corresponding available ADMis on the list of excluded ADMs; and assigning a high modified demeritnumerical value to the corresponding available ADM; and replacing thetotal demerit numerical value for the corresponding available ADM withthe assigned high modified demerit value.
 8. The computer-implementedmethod of claim 1, wherein the operations further comprise transmittinga therapy regimen from the data processing hardware to a patient deviceassociated with the patient and in communication with the dataprocessing hardware via the network, the therapy regimen comprising therecommended ADMs and the recommended dosage for each recommended ADM,the therapy regimen when received by the patient device causing thepatient device to display the recommended ADMs and the recommendeddosage for each recommended ADM on a patient interface executing on thepatient device
 9. The computer-implemented method of claim 1, whereinthe administration device comprises a smart pill bottle and the dosercomprises a locking/dispensing mechanism configured dispense one or moreADM pills based on the recommended dosage.
 10. The computer-implementedmethod of claim 1, wherein the administration device comprises a smartpen including a cartridge containing the recommended ADM and the dosercomprises a needle for insertion into the patient for administering therecommended ADM to the patient via the cartridge.
 11. A systemcomprising: data processing hardware; and memory hardware incommunication with the data processing hardware and storing instructionsthat when executed on the data processing hardware causes the dataprocessing hardware to perform operations comprising: receiving patientinformation associated with a patient seeking selection and dosing ofone or more of the available ADMs, the patient information comprising: acurrent medications list including a list of medications andcorresponding dosages the patient is currently prescribed; and medicalconditions associated with the patient; obtaining treatment guidelineratings each assigned by the patient that measures a subjective level ofimportance to the patient for a corresponding treatment guideline, thetreatment guideline ratings including at least one of a cost rating, abody weight rating, a treatment regimen complexity rating, a treatmentefficacy rating, a mealtime coverage needs rating, or a hypoglycemiarating; for each corresponding available ADM of the plurality ofavailable ADMs: obtaining scaled guideline values for the correspondingavailable ADM based on the prescribing drug information and thepublished guidelines, each scaled guideline value associated with acorresponding treatment guideline rating; determining a guidelinedemerit numerical value for the corresponding available ADM bymultiplying, for each treatment guideline rating, the treatmentguideline rating times the corresponding scaled guideline value and aguideline demerit increment value; and determining a total demerit valuefor the corresponding available ADM based on the guideline demeritnumerical value determined for the corresponding available ADM; orderingthe total demerit numerical values in numerical order for the availableADMs from the lowest total demerit numerical value to the highest totaldemerit numerical value; selecting a predetermined number of recommendedADMs associated with the lowest total numerical demerit values from theplurality of available ADMs; determining a recommended dosage for eachrecommended ADM based on the patient information, the prescribing druginformation, and the published guidelines; and transmitting, via anetwork, instructions to an administration device associated with one ofthe recommended ADMs, the instructions when received by theadministration device causing the administration device to configure adoser of the administration device to administer the recommended dosageof the one of the recommended ADMs to the patient.
 12. The system ofclaim 11, wherein the patient information further comprises at least oneof: a target glucose range for the patient; a target A1c value for thepatient; a preferred minimum monthly treatment cost; or a preferredmaximum monthly treatment cost.
 13. The system of claim 11, wherein thepatient information further comprises treatment guideline ratings eachassigned by the patient that measures a subjective level of importanceto the patient for a corresponding treatment guideline, the treatmentguideline ratings comprising at least one of a cost rating, a bodyweight rating, a treatment regimen complexity rating, a treatmentefficacy rating, a mealtime coverage needs rating, or a hypoglycemiarating.
 14. The system of claim 11, wherein the patient informationfurther comprises at least one of: one or more glucose values for thepatient measured by a glucometer or continuous glucose monitor incommunication with the data processing hardware; or an A1c value for thepatient.
 15. The system of claim 11, wherein the operations furthercomprise: receiving exercise data from a fitness tracker associated withthe patient; and adjusting the recommended dosage for at least one ofthe recommended ADMs based on the received exercise data.
 16. The systemof claim 11, wherein the operations further comprise: obtainingprescribing drug information and published guidelines for each of aplurality of available Anti-Diabetes Medications (ADMs) for managingglucose levels, the prescribing drug information and publishedguidelines comprising, for each corresponding available ADM of theplurality of available ADMs: one or more contraindicating conditionsassociated with the corresponding available ADM; and a list ofmedications that interact with the corresponding available ADM; and foreach corresponding available ADM of the plurality of available ADMs:assigning an adverse demerit increment value when the medical conditionsassociated with the patient indicate that the patient currently has anyof the contraindicating conditions associated with the correspondingavailable ADM; and assigning the adverse demerit increment value whenthe current medications list indicate that the patient is currentlytaking at least one of the medications that interact with correspondingavailable ADM; wherein determining the total demerit numerical value forthe corresponding available ADM is further based on a sum of eachadverse demerit increment value assigned to the corresponding availableADM.
 17. The system of claim 11, wherein the operations furthercomprise: obtaining a list of excluded ADMs that the patient is eitherallergic to or is excluded from the treatment regimen for the patient;and for at least one corresponding available ADM of the plurality ofavailable ADMs: determining the corresponding available ADM is on thelist of excluded ADMs; and assigning a high modified demerit numericalvalue to the corresponding available ADM; and replacing the totaldemerit numerical value for the corresponding available ADM with theassigned high modified demerit value.
 18. The system of claim 11,wherein the operations further comprise transmitting a therapy regimenfrom the data processing hardware to a patient device associated withthe patient and in communication with the data processing hardware viathe network, the therapy regimen comprising the recommended ADMs and therecommended dosage for each recommended ADM, the therapy regimen whenreceived by the patient device causing the patient device to display therecommended ADMs and the recommended dosage for each recommended ADM ona patient interface executing on the patient device
 19. The system ofclaim 11, wherein the administration device comprises a smart pillbottle and the doser comprises a locking/dispensing mechanism configureddispense one or more ADM pills based on the recommended dosage.
 20. Thesystem of claim 11, wherein the administration device comprises a smartpen including a cartridge containing the recommended ADM and the dosercomprises a needle for insertion into the patient for administering therecommended ADM to the patient via the cartridge.